Intro:
Hi and welcome to my final project presentation. Below I will build
graphs to replicate that of a scientific report published by Christine
Nicol of Royal Veterinary College and Claire Weeks of University of
Bristol. Their report is about the widespread issue of injurious pecking
among laying hens. They are looking at the ability of hens in captivity
to care for their beaks accordingly when generations have not been doing
so and in the case that they can, what tools would need to be present in
the pen.
Initially, my plan was to make a personal website detailing the movies that I had watched during j-term and ranking them accordingly, but that proved to be quite difficult. Since I could only start on the final project post our class on Tuesday I was not able to get any troubleshooting help, so I decided making graphs was the next best thing.
Step 1: obtain the data
1. Download the data set from datadryad.org. I used this website for the
beak data: https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.2bvq83bs3
2. Click the link provided that will guide you to orchid.org to find the
report figures. - insert pic 3. Under works you will find title that
relates to pecking and beaks. Click the DOI code. - insert pic 4. Now
that you have open access to the article, you can scroll a bit down and
clock browse figure. This will show you all the figures within the
article so you know what to copy. Take screenshots of the pictures so
you can refer back to them as you work on the graph replication.
Step 2: Upload your data: 1.
library(ggplot2)
library(ggthemes)
library(patchwork)
library(plyr)
Quail1<- read.table(file="final project/Trans-ovo quail 15.csv", header = TRUE, sep = ",")
head(Quail1)## Nest Permethrin Number Heart.Mass..g. Heart.Length..mm.
## 1 .2 g cotton Control 1 0.0334 5.29
## 2 .2 g cotton Control 2 0.0355 6.23
## 3 .2 g cotton Control 3 0.0206 4.83
## 4 .2 g cotton Control 4 0.0337 6.53
## 5 .2 g cotton Control 5 0.0251 5.29
## 6 .2 g cotton Control 6 0.0312 5.66
## Heart.Width..mm. Heart.Depth..mm. Mass..g. Right.Tarsus..mm. Beak..mm.
## 1 3.90 3.04 3.9571 12.54 5.137
## 2 3.90 3.63 4.3403 13.07 6.370
## 3 3.21 3.16 3.2672 10.78 5.344
## 4 4.58 3.32 4.8308 13.25 4.655
## 5 3.74 2.49 3.3593 11.59 4.938
## 6 3.69 3.08 4.5668 13.51 6.669
## Toe..mm. Eye.Diameter..mm. Head.Diameter..mm. Heart.rates
## 1 8.556757 4.668 15.900 264
## 2 8.966975 4.587 15.771 338
## 3 8.401623 4.085 17.166 NA
## 4 9.539726 3.855 17.646 285
## 5 8.612118 4.122 15.075 NA
## 6 10.761066 4.267 15.776 209Quail2<- read.table(file="final project/Trans-ovo qual 5.csv", header = TRUE, sep = ",")
head(Quail2)## Embryo.Number Nest.Cotton.Weight Dose Treatment Mass Heart.Rate Stage
## 1 1 0.8 0 Control 0.38 132.6 33
## 2 2 0.8 0 Control 0.51 84.0 32
## 3 3 0.8 0 Control 0.48 105.0 31
## 4 4 0.8 0 Control 0.40 102.3 33
## 5 5 0.8 0 Control 0.48 110.0 31
## 6 6 0.8 0 Control 0.58 106.6 33
## Head.Width..mm. Eye.Width..mm. Body.Length..mm. Hindbrain..Fluid.Solid
## 1 7.214 4.396 17.685 Fluid
## 2 5.810 3.131 16.984 Fluid
## 3 7.173 3.459 18.606 Fluid
## 4 6.644 5.016 17.518 Fluid
## 5 7.579 4.590 15.673 Fluid
## 6 10.853 6.375 18.755 Fluid
## Forebrain..Fluid.Solid Forebrain.Width..mm. Forebrain.Height..mm.
## 1 Fluid 3.716 1.078
## 2 Fluid 2.981 0.740
## 3 Fluid 3.462 0.990
## 4 Fluid 3.262 0.959
## 5 Solid 3.075 1.110
## 6 Fluid 4.054 0.873
## Forebrain.Area..cm2. Hindbrain.Width..cm. Hindbrain.width..mm.
## 1 4.005848 0.136 1.36
## 2 2.205940 0.211 2.11
## 3 3.427380 0.101 1.01
## 4 3.128258 0.120 1.20
## 5 3.413250 0.200 2.00
## 6 3.539142 0.117 1.17
## Hindbrain.Height..mm. Hindbrain.Area..cm2. Ventricular.Apex Atrium
## 1 3.16 0.042976 pointed 1
## 2 3.32 0.070052 NA
## 3 3.32 0.033532 pointed 2
## 4 2.47 0.029640 pointed 2
## 5 3.71 0.074200 round 0
## 6 2.56 0.029952 pointed 2
## Heart.Weight..g.
## 1 0.0039
## 2 NA
## 3 0.0041
## 4 0.0008
## 5 0.0002
## 6 0.0032Step 3: Figure 1 Here is a picture of my intended replication: 
### Making graph 1
###### looking at my data given in the form of a data frame I labeled Quail1, I can see that the first graph in figure 1 uses columns: Treatment and Mass. I know this because the while the treatment column matches the permethrin column in Quail1, the categorizing column (nest weight in cotton) is only present in Quail2 for both .8g and .2g.
#making my SEM bars
#data_summary <- function(data, varname, groupnames){
# require(plyr)
# summary_func <- function(x, col){
# c(mean = mean(x[[col]], na.rm=TRUE),
# sd = sd(x[[col]], na.rm=TRUE))
# }
# data_sum<-ddply(data, groupnames, .fun=summary_func,
# varname)
# data_sum <- rename(data_sum, c("mean" = varname))
# return(data_sum)
#}
#df2 <- data_summary(Quail1, varname="Mass..g.",
# groupnames=c("Permethrin", "Nest"))
# Convert Nest to a factor variable
#df2$Nest=as.factor(df2$Nest)
#head(df2)
# reverse order of legend
#df2$Nest <- factor(df2$Nest, levels = rev(levels(df2$Nest)))
#labels for legend
L <- c("A","A","B","C")
l1<- c(5,4,3,2.5)
#make the graph
graph1<- ggplot(data=Quail1,
aes(x= Permethrin, y=Mass..g., fill=Nest)) + geom_bar(position="dodge", stat="identity", width=0.5) +
ylab("Body mass(g)") +
xlab("")+
labs( tag= "(A)")
stat_summary(fun.data = mean_se,
geom="errorbar",
width = 0.2)## geom_errorbar: na.rm = FALSE, orientation = NA, width = 0.2
## stat_summary: fun.data = function (x, mult = 1)
## {
## x <- stats::na.omit(x)
## se <- mult * sqrt(stats::var(x)/length(x))
## mean <- mean(x)
## data_frame0(y = mean, ymin = mean - se, ymax = mean + se, .size = 1)
## }, fun = NULL, fun.max = NULL, fun.min = NULL, fun.args = list(), na.rm = FALSE, orientation = NA
## position_identitygraph1 + theme_classic() + scale_fill_grey( start = .1, end = .3, breaks = c("0.2 g cotton", "0.8 g cotton")) + guides(fill=guide_legend(title=""),) +
theme(axis.text = element_text(face="bold"), panel.background = element_rect(fill = 'gray94', color = 'black'),
legend.position="left") 
### Making graph 2
###### looking at my data given in the form of a data frame I labeled Quail1, I can see that the first graph in figure 1 uses columns: Treatment and Right.Tarsus. I know this because the while the treatment column matches the Permethrin column in Quail2, the dependent column (Tarsus length) is only present in Quail1.
#reverse the legend
Quail1$Nest <- factor(Quail1$Nest, levels = rev(levels(Quail1$Nest)))
#make the graph
graph2<- ggplot(data=Quail1,
aes(x= Permethrin, y=Right.Tarsus..mm., fill=Nest)) + geom_bar(position="dodge", stat="identity", width=0.5) +
ylab("Tarsus Length (mm)") +
xlab("") +
labs( tag="(B)")
graph2 + theme_classic() + scale_fill_grey( start = .1, end = .3, breaks = c("0.2 g cotton", "0.8 g cotton")) + guides(fill=guide_legend(title=""),) +
theme(axis.text = element_text(face="bold"), panel.background = element_rect(fill = 'gray94', color = 'black'),
legend.position="left")
Step 4: Figure 2 Here is a picture of my intended replication: 
Step 5: Figure 3
Here is a picture of my intended replication: 
Step 6: Figure 4 Here is a picture of my intended replication: 
Step 10: Plausible troubleshooting: